Method and apparatus for iteratively detecting and decoding signal in communication system with multiple-input and multiple-out (mimo) channel

ABSTRACT

A communication apparatus with a multiple-input and multiple-output (MIMO) channel, includes a minimum mean square error (MMSE) detector configured to estimate quadrature amplitude modulation (QAM) symbols based on signals received through the MIMO channel. The apparatus further includes a QAM demodulator configured to demodulate the estimated QAM symbols, and estimate a first posterior probability of each of encoded bits of the estimated QAM symbols, and a first module configured to remove a first prior probability of each of the encoded bits from the first posterior probability to generate soft estimates of the encoded bits. The apparatus further includes a channel decoder configured to decode the encoded bits based on the soft estimates, and generate an improved posterior probability of each of the encoded bits, and a second module configured to generate a second prior probability of each of the encoded bits based on the improved posterior probability.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 USC 119(a) of RussianPatent Application No. 2012150727, filed on Nov. 27, 2012, in theRussian Federal Service for Intellectual Property, and Korean PatentApplication No. 10-2013-0123202, filed on Oct. 16, 2013, in the KoreanIntellectual Property Office, the entire disclosures of which areincorporated herein by reference for all purposes.

BACKGROUND

1. Field

The following description relates to a method and an apparatus foriteratively detecting and decoding a signal in a communication systemwith a multiple-input and multiple-output (MIMO) channel.

2. Description of Related Art

Multiple-input and multiple-output (MIMO) refers to a smart antennatechnology that enhances a capacity for wireless communication. MIMOsystems have been developed in a field of wireless communicationsystems.

In MIMO systems, a plurality of antennas may be used in a base stationand/or a terminal, and accordingly, a capacity may be increased inproportion to a number of the used antennas. MIMO systems may paralleltransmit signals through a plurality of various channels in the samefrequency band. Due to parallel transmission of signals, MIMO systemsmay demonstrate an extremely high spectral efficiency.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, a communication apparatus with a multiple-inputand multiple-output (MIMO) channel, includes a minimum mean square error(MMSE) detector configured to estimate quadrature amplitude modulation(QAM) symbols based on signals received through the MIMO channel. Theapparatus further includes a QAM demodulator configured to demodulatethe estimated QAM symbols, and estimate a first posterior probability ofeach of encoded bits of the estimated QAM symbols, and a first moduleconfigured to remove a first prior probability of each of the encodedbits from the first posterior probability to generate soft estimates ofthe encoded bits. The apparatus further includes a channel decoderconfigured to decode the encoded bits based on the soft estimates, andgenerate an improved posterior probability of each of the encoded bits,and a second module configured to generate a second prior probability ofeach of the encoded bits based on the improved posterior probability,the second prior probability being a first prior probability of each ofencoded bits of QAM symbols in a next iteration cycle. The apparatusfurther includes a hard-decision estimator configured to generate asequence of hard estimates of information bits based on the improvedposterior probability.

The first posterior probability may include a logarithm ratio ofposterior probabilities of each of the encoded bits, and the first priorprobability may include a logarithm ratio of prior probabilities of eachof the encoded bits.

The first prior probability may include a natural logarithm of a valueobtained by dividing a probability that a k-th encoded bit in an n-thQAM symbol is 1, by a probability that the k-th encoded bit in the n-thQAM symbol is −1, and n may be an integer that is greater than or equalto 1, and less than or equal to N. k may be an integer that is greaterthan or equal to 1, and less than or equal to K, N may denote a numberof the QAM symbols, and K may denote a number of bits in each of the QAMsymbols.

The improved posterior probability may include a logarithm ratio ofimproved posterior probabilities of each of the encoded bits, and thesecond module may be configured to remove the soft estimates from theimproved posterior probability to generate the second prior probability.In response to the improved posterior probability being generated atleast one time, the first module may be further configured to generatesoft estimates of the encoded bits in the next iteration cycle based onthe second prior probability as the first prior probability in the nextiteration cycle.

The communication apparatus may further include a remodulator configuredto generate a first mathematical expectation and a first variance of theQAM symbols based on the improved posterior probability, and a thirdmodule configured to compare the first variance with a third variancegenerated after estimation of QAM symbols in a previous iteration cycle,and generate input parameters of the MMSE detector based on a result ofthe comparing.

The input parameters may include a second mathematical expectation and asecond variance of the QAM symbols, and a third mathematical expectationmay be generated after the estimation of the QAM symbols in the previousiteration cycle. In response to the first variance being less than thethird variance, the third module may be configured to generate thesecond mathematical expectation based on the first mathematicalexpectation, the first variance, the third mathematical expectation, andthe third variance, and generate the second variance based on the firstvariance and the third variance. In response to the first variance beinggreater than or equal to the third variance, the third module may beconfigured to generate the second mathematical expectation and thesecond variance in a current iteration cycle, as a second mathematicalexpectation and a second variance of QAM symbols in the previousiteration cycle.

The MMSE detector may be configured to apply an MMSE linear filter to asymbol vector of the QAM symbols to generate an MMSE estimationmathematical expectation and an MMSE estimation variance of the symbolvector, generate a third mathematical expectation to be used in the nextiteration cycle, based on the second mathematical expectation, thesecond variance, the MMSE estimation mathematical expectation, and theMMSE estimation variance, generate a third variance to be used in thenext iteration cycle, based on the second variance and the MMSEestimation variance, and estimate QAM symbols in the next iterationcycle in response to the third mathematical expectation and the thirdvariance being generated. The third mathematical expectation and thethird variance to be used in the next iteration cycle may be input tothe QAM demodulator in the next iteration cycle.

The QAM demodulator may be further configured to estimate a firstposterior probability of each of the encoded bits in the next iterationcycle based on the third mathematical expectation and the third varianceto be used in the next iteration cycle.

In response to the third mathematical expectation and the third variancebeing initially generated, the third module may be configured to set thesecond mathematical expectation to 0, and set the second variance to aunit variance.

The communication apparatus may further include a deinterleaverconfigured to deinterleave the encoded bits before the encoded bits areinput to the channel decoder, and an interleaver configured tointerleave soft bits output from the channel decoder.

In another general aspect, a method in a communication apparatus with amultiple-input and multiple-output (MIMO) channel, includes estimatingquadrature amplitude modulation (QAM) symbols based on signals receivedthrough the MIMO channel. The method further includes demodulating theestimated QAM symbols, and estimating a first posterior probability ofeach of encoded bits of the estimated QAM symbols. The method furtherincludes removing a first prior probability of each of the encoded bitsfrom the first posterior probability to generate soft estimates of theencoded bits, and decoding the encoded bits based on the soft estimates.The method further includes generating an improved posterior probabilityof each of the encoded bits, and generating a second prior probabilityof each of the encoded bits based on the improved posterior probability,the second prior probability being a first prior probability of each ofencoded bits of QAM symbols in a next iteration cycle. The methodfurther includes generating a sequence of hard estimates of informationbits based on the improved posterior probability.

The generating of the second prior probability may include removing thesoft estimates from the improved posterior probability to generate thesecond prior probability. The method may further include, in response tothe improved posterior probability being generated at least one time,generating soft estimates of the encoded bits in the next iterationcycle based on the second prior probability as the first priorprobability in the next iteration cycle.

The method may further include generating a first mathematicalexpectation and a first variance of the QAM symbols based on theimproved posterior probability, and comparing the first variance with athird variance generated after estimation of QAM symbols in a previousiteration cycle. The method may further include generating inputparameters for estimation of the QAM symbols in the next iteration cyclebased on a result of the comparing.

The generating of the input parameters may include, in response to thefirst variance being less than the third variance, generating the secondmathematical expectation based on the first mathematical expectation,the first variance, the third mathematical expectation, and the thirdvariance, and generating the second variance based on the first varianceand the third variance, and the generating of the input parameters mayinclude, in response to the first variance being greater than or equalto the third variance, generating the second mathematical expectationand the second variance in a current iteration cycle, as a secondmathematical expectation and a second variance of QAM symbols in theprevious iteration cycle.

The estimating of the QAM symbols may include applying a minimum meansquare error (MMSE) linear filter to a symbol vector of the QAM symbolsto generate an MMSE estimation mathematical expectation and an MMSEestimation variance of the symbol vector, and generating a thirdmathematical expectation to be used in the next iteration cycle, basedon the second mathematical expectation, the second variance, the MMSEestimation mathematical expectation, and the MMSE estimation variance.The estimating of the QAM symbols may further include generating a thirdvariance to be used in the next iteration cycle, based on the secondvariance and the MMSE estimation variance, and estimating QAM symbols inthe next iteration cycle in response to the third mathematicalexpectation and the third variance being generated.

The method may further include estimating a first posterior probabilityof each of the encoded bits in the next iteration cycle based on thethird mathematical expectation and the third variance to be used in thenext iteration cycle.

The method may further include, in response to the third mathematicalexpectation and the third variance being initially generated, settingthe second mathematical expectation to 0, and setting the secondvariance to a unit variance.

In still another general aspect an apparatus includes a processorconfigured to estimate quadrature amplitude modulation (QAM) symbolsbased on signals, demodulate the estimated QAM symbols, and estimate aposterior probability of each of encoded bits of the estimated QAMsymbols. The processor is further configured to remove a priorprobability of each of the encoded bits from the posterior probabilityto generate soft estimates of the encoded bits, and decode the encodedbits based on the soft estimates.

The processor may be further configured to generate an improvedposterior probability of each of the encoded bits, generate anotherprior probability of each of encoded bits of QAM symbols in a nextiteration cycle based on the improved posterior probability, andgenerate a sequence of hard estimates of information bits based on theimproved posterior probability.

The processor may be further configured to generate a first mathematicalexpectation and a first variance of the QAM symbols based on theimproved posterior probability, generate a second mathematicalexpectation and a second variance of the QAM symbols based on the firstmathematical expectation, the first variance, a third mathematicalexpectation of QAM symbols in a previous iteration cycle, and a thirdvariance of the QAM symbols in the previous iteration cycle, andestimate QAM symbols in the next iteration cycle based on the secondmathematical expectation and the second variance.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a transmission apparatuswith a multiple-input and multiple-out (MIMO) channel and configured togenerate a signal.

FIG. 2 is a diagram illustrating an example of a reception apparatuswith a MIMO channel.

FIG. 3 is a diagram illustrating an example of a reception apparatusconfigured to perform iterative detection and decoding a signal.

FIG. 4 is a flowchart illustrating an example of a method of iterativelydetecting and decoding a signal in a communication apparatus with a MIMOchannel.

FIG. 5 is a flowchart illustrating an example of an operation ofestimating quadrature amplitude modulation (QAM) symbols in the methodof FIG. 4.

FIG. 6 is a diagram illustrating an example of a communication apparatuswith a MIMO channel and configured to iteratively detect and decode asignal.

FIG. 7 is a diagram illustrating an example of results of iterativedetection and decoding of a signal.

FIG. 8 is a diagram illustrating another example of results of iterativedetection and decoding of a signal.

Throughout the drawings and the detailed description, unless otherwisedescribed or provided, the same drawing reference numerals will beunderstood to refer to the same elements, features, and structures. Thedrawings may not be to scale, and the relative size, proportions, anddepiction of elements in the drawings may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the systems, apparatuses and/ormethods described herein will be apparent to one of ordinary skill inthe art. The progression of processing steps and/or operations describedis an example; however, the sequence of and/or operations is not limitedto that set forth herein and may be changed as is known in the art, withthe exception of steps and/or operations necessarily occurring in acertain order. Also, descriptions of functions and constructions thatare well known to one of ordinary skill in the art may be omitted forincreased clarity and conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided so thatthis disclosure will be thorough and complete, and will convey the fullscope of the disclosure to one of ordinary skill in the art.

Various modes to transmit signals by multiple-input and multiple-output(MIMO) may be used. Among the various modes, a Vertical-BellLaboratories Layered Space-Time (V-BLAST) architecture may be used.

In the V-BLAST architecture known as a spatial multiplexing scheme, asignal may be divided into a plurality of parallel streams. Theplurality of parallel streams may be transmitted by a plurality ofspatial channels formed between sets of transmitting antennas andreceiving antennas.

A MIMO system will be described in a frequency domain by a matrixequation, for example, Equation 1 below.

Y=HX+η  [Equation 1]

In Equation 1, Y denotes an M-dimensional vector of a complex valuesignal on an input of a MIMO detector, and may be regarded as a vectorof output samples of a MIMO channel. Additionally, X denotes anN-dimensional vector of quadrature amplitude modulation (QAM) symbolsthat are modulated and transmitted in a transmitter, and may be regardedas a vector of input samples of the MIMO channel. η denotes a vector ofvalues of complex noise, and H denotes a complex channel matrix of theMIMO channel and has a size of “M×N”. Each of M and N may be an integerthat is greater than or equal to “1”.

When a signal is transmitted using the spatial multiplexing scheme, amutual interference between different parallel data streams may occur.Accordingly, a task to design efficient methods of signal detection,demodulation and decoding in a condition of an outer noise and mutualinterference may occur. A method for signal detection may be a maximumlikelihood (ML) method. The ML method may be used to estimate aprobability of all possible combinations of symbols. A main drawback ofthe ML method may be a high complexity that may rise exponentiallyversus a number of simultaneously transmitted streams. A large number ofalternative methods for signal detection due to a mitigation of mutualinterference may exist. A method used to mitigate an interference mayinclude, for example, a zero forcing (ZF) method and a minimum meansquare error (MMSE) method. The ZF method may be used to minimize a meaninterference power in a receiver, and the MMSE method may be used tominimize a mean square error of estimation.

In the MMSE method, Equation 1 may be defined to be Equation 2 below.

Y=G _(MMSE) ⁻¹ {tilde over (X)} _(MMSE)  [Equation 2]

G_(MMSE) may be defined in Equation 3 below.

$\begin{matrix}{G_{MMSE} = {( {{H^{H}H} + {\frac{\sigma_{\eta}^{2}}{\sigma_{s}^{2}}I_{N}}} )^{- 1}H^{H}}} & \lbrack {{Equation}\mspace{14mu} 3} \rbrack\end{matrix}$

In Equations 2 and 3, G_(MMSE) denotes an MMSE filter matrix of lineartransformation σ_(η) ² denotes a noise varience, and σ_(s) ²• denotes amean value of a signal power. Additionally, I_(N) denotes an identitymatrix with a size of “N×N”, and “N” may be an integer that is greaterthan or equal to “1”. {tilde over (X)}_(MMSE) denotes a vector of MMSEestimation of transmitted QAM symbols, and may be an MMSE solution.

The ZF method and the MMSE method may have relatively low realizationcomplexity. However, the ZF method and the MMSE method may demonstrate asignificant degradation, compared with the ML method, in an accuracy ofestimation of received symbols, and ultimately, in a probability ofreceiving of incorrect bits in a message (namely, a bit error rate(BER)).

Modern digital communication systems employing a spatial multiplexingscheme may widely propose other methods of transmitting and receiving asignal to improve an efficiency and quality of signal transmission anddata transmission. For example, methods of channel encoding of digitaldata in a transmission device, and methods of decoding of digital datain a receiver may be widely applied in the above methods. Additionally,the methods may use data scrambling and interleaving of data, designatedby the term “interleaving”. Interleaving may lead to more efficientdistribution of transmitted signals in a spatial-time-frequencycontinuum. The more efficient distribution of transmitted signals mayfinally improve a quality of data transmission.

In methods of iteratively detecting and decoding a signal, jointdetection and decoding may be performed within an iterative process. Theabove methods may also be called “turbo processing methods”.

In a communication system, digital data may be exposed to a codingprocedure in a channel coder. In the channel coder, an initial inputsequence of information bits may be transformed to an output sequence.The output sequence may include additional parity check bits.

Various types of channel codes may be used in digital communicationsystems. For example, convolution codes, turbo codes, and low densityparity check (LDPC) codes may be widely, practically applied.

After encoding, data may be interleaved, may be modulated, may bespatially de-multiplexed, may be transformed from a digital signal to ananalog signal, may be frequency-converted, may be amplified, and may bespatially transmitted by a plurality of antennas of a transmitter.

FIG. 1 illustrates an example of a transmission apparatus 100 with aMIMO channel and configured to generate a signal. FIG. 1 illustrates anexample of signal generation in a system with the MIMO channel.

In FIG. 1, the transmission apparatus 100 includes a channel coder 101,an interleaver 103, a modulator 105, a demultiplexer 107, and aplurality of transmitters, namely, “N” transmitters. The plurality oftransmitters may include, for example, transmitters 109 a and 109 b. “N”denotes a number of the transmitters, and may be an integer that isgreater than or equal to “2”.

Input data may be bits, for example, input information bits.

The channel coder 101 encodes the input information bits to generate theencoded bits. That is, the channel coder 101 transforms the inputinformation bits to the encoded bits.

The interleaver 103 interleaves the encoded bits to generate theinterleaved bits.

The modulator 105 modulates the interleaved bits to generate modulateddata. The modulator 105 may be, for example, a mapper.

The demultiplexer 107 demultiplexes the modulated data to split themodulated data into a plurality of spatial streams.

Each of the plurality of transmitters may transform each of theplurality of spatial streams to an analog signal. Each of the pluralityof transmitters may convert a carrier frequency of the analog signal,and may amplify the analog signal. After the transforming, theconverting, and the amplifying, each of the plurality of transmittersmay transmit analog signals of the respective plurality of spatialstreams.

Hereinafter, a reception apparatus corresponding to the transmissionapparatus 100 of FIG. 1 will be further described with reference to FIG.2.

FIG. 2 illustrates an example of a reception apparatus 200 with a MIMOchannel. FIG. 2 illustrates an example of signal reception in thereception apparatus 200 with the MIMO channel.

In FIG. 2, the reception apparatus 200 includes a plurality ofreceivers, a MIMO detector 203, a demodulator 205, a deinterleaver 207,and a channel decoder 209. The plurality of receivers may include, forexample, receivers 201 a and 201 b. The plurality of receivers may be“M” receivers, and “M” may be an integer that is greater than or equalto “2”.

Each of the plurality of receivers may receive each of transmittedsignals. Each of the plurality of receivers may amplify the receivedsignals, may transform the received signals, using a carrier frequency,may filter the received signals, and may transform the received signalsto digital signals. The receiving, the amplifying, the transformingusing the carrier frequency, the filtering, and the transforming todigital signals may be sequentially performed on each of the transmittedsignals in a corresponding receiver.

The digital signals output from the plurality of receivers may betransmitted to the MIMO detector 203. The MIMO detector 203 performspreliminary MIMO detection of the digital signals, and outputs thedigital signals. The MIMO detector 203 may detect a multidimensionalinput signal from the digital signals.

The demodulator 205 demodulates the digital signals processed by theMIMO detector 203, to generate the demodulated digital signals.

The deinterleaver 207 deinterleaves the demodulated digital signals togenerate the deinterleaved digital signals.

The channel decoder 209 decodes the deinterleaved digital signals togenerate output data.

The output data may correspond to the input data and the inputinformation bits of FIG. 1. The deinterleaved digital signals maycorrespond to the encoded bits of FIG. 1. The demodulated digitalsignals may correspond to the interleaved bits of FIG. 1. The digitalsignals output from the MIMO detector 203 may correspond to themodulated data of FIG. 1. The digital signals output from the pluralityof receivers may correspond to the plurality of spatial streams ofFIG. 1. The transmitted signals received by the plurality of receiversmay correspond to the analog signals transmitted by the plurality oftransmitters of FIG. 1.

FIG. 3 illustrates an example of a reception apparatus 300 configured toperform iterative detection and decoding of a signal. The receptionapparatus 300 of FIG. 3 performs the iterative detection and decoding ina system with a MIMO channel.

The reception apparatus 300 may correspond to the reception apparatus200 of FIG. 2. In the reception apparatuses 300 and 200, elements withsame or similar names may perform same or similar functions.

Referring to FIG. 3, the reception apparatus 300 includes a plurality ofreceivers, a MIMO detector 304, a deinterleaver 307, a channel decoder309, an interleaver 311, and a hard-decision estimator 313. The MIMOdetector 304 includes a demapper 303 and a demodulator 305. Theplurality of receivers may include, for example, receivers 301 a and 301b. The plurality of receivers may be “M” receivers, and “M” may be aninteger that is greater than or equal to“2”.

Each of the plurality of receivers may receive each of transmittedsignals. Each of the plurality of receivers may amplify the receivedsignals, may transform the received signals, using a carrier frequency,may filter the received signals, and may transform the received signalsto digital signals. The receiving, the amplifying, the transformingusing the carrier frequency, the filtering, and the transforming todigital signals may be sequentially performed on each of the transmittedsignals in a corresponding receiver.

The digital signals output from the plurality of receivers may betransmitted to the demapper 303 of the MIMO detector 304. The MIMOdetector 304 (e.g., the demapper 303) may detect a multidimensionalinput signal from the digital signals.

The demapper 303 receives, together with the digital signals, priorinformation of encoded bits, namely, QAM symbols including the encodedbits. The prior information is output from the interleaver 311. Theprior information may include a prior probability of the encoded bits.The encoded bits after estimation of a probability of the encoded bits,namely, a logarithm ratio of posterior probabilities, may be called softbits.

The demodulator 305 demodulates the detected multidimensional inputsignal to generate encoded bits. The demodulator 305 performs a firstestimation of a probability of the encoded bits. The demodulator 305 mayperform the first estimation, using a logarithm likelihood ratio (LLR)or a logarithm ratio of posterior probabilities.

In an example, the MIMO detector 304 and the demodulator 305 may beclosely integrated. The MIMO detector 304 and the demodulator 305 may becombined in a joint module, namely, the demapper 303.

The soft bits are input to the deinterleaver 307. The deinterleaver 307deinterleaves the soft bits to generate the deinterleaved soft bits. Thedeinterleaved soft bits are input to the channel decoder 309.

The channel decoder 309 estimates a posterior probability of informationbits, namely, a logarithm ratio of posterior probabilities. Theinformation bits are output via a first output, namely, a main output,of the channel decoder 309, based on parameters of a used code. Forexample, the channel decoder 309 may generate soft estimates of theinformation bits.

Additionally, the channel decoder 309 improves values of probabilitiesof encoded bits output via a second output of the channel decoder 309.For example, the channel decoder 309 may generate improved softestimates of the encoded bits.

The first output of the channel decoder 309 may include the softestimates of the information bits. The hard-decision estimator 313transforms the soft estimates of the information bits to hard estimatesof the information bits, to generate output data. An output of thehard-decision estimator 313 may be a common output of a communicationapparatus with a MIMO channel.

The second output of the channel decoder 309 may include the improvedsoft estimates of the encoded bits. When the improved soft estimates ofthe encoded bits are removed from information existing in a form of softestimates of the encoded bits included in an input of the channeldecoder 309, the interleaver 311 interleaves the improved soft estimatesof the encoded bits. The interleaved soft estimates are output to anauxiliary input of the MIMO detector 304 as the prior information for anext iteration.

In an example in which a spherical detection method is used, the priorinformation may be input to the MIMO detector 304, to reduce a number ofsymbols-candidates participating in computation of posteriorinformation. In another example in which an ML detector is used, priorinformation may be input to the demodulator 305.

In the next iteration, the MIMO detector 304 and the demodulator 305generate a new estimate of a posterior probability of the encoded bits,namely, a logarithm ratio of posterior probabilities. When the priorinformation, namely, a prior logarithm ratio of prior probabilities isremoved from the new estimate, the deinterleaver 307 deinterleaves thenew estimate from which the prior information is removed. The channeldecoder 309 decodes the deinterleaved new estimate. The new estimate maybe deinterleaved and decoded one more time. Repeating an iterativeprocedure of detection by the MIMO detector 304 through decoding by thechannel decoder 309 may lead to improving a reliability of estimates ofencoded bits. By improving the reliability, a final sequence of hardestimates of the information bits may be matched to a transmittedsequence with a higher probability. The final sequence may correspond tothe output data of the reception apparatus 300. The output data of thereception apparatus 300 may correspond to the input data of thetransmission apparatus 100 of FIG. 1.

To iteratively perform detection and decoding of a signal in atransmission apparatus with a MIMO channel and a reception apparatuswith a MIMO channel, at least one method may be used. Various methodsfor iterative detection and decoding of a signal may be chosen as aprototype of examples.

A scheme of receiving and processing a signal may correspond to a schemeof FIG. 3. Accordingly, when processing is performed by the MIMOdetector 304, a posterior probability may be estimated based on an MLalgorithm of Equation 4.

$\begin{matrix}{{L( b_{n,k} )} = {{\ln( \frac{\sum\limits_{x:{f{({b_{n,k} = 1})}}}{\prod\limits_{m = 1}^{M}{{\exp ( \beta_{m} )}{\prod\limits_{p = 1}^{N}{\prod\limits_{\underset{{t \neq {k\mspace{14mu} {when}\mspace{14mu} p}} = n}{t = 1}}^{K}{\Pr ( b_{p,t} )}}}}}}{\sum\limits_{x:{f{({b_{n,k} = {- 1}})}}}^{M}{{\exp ( \beta_{m} )}{\prod\limits_{p = 1}^{N}{\prod\limits_{\underset{{t \neq {k\mspace{14mu} {when}\mspace{14mu} p}} = n}{t = 1}}^{K}{\Pr ( b_{p,t} )}}}}} )} + {\ln ( \frac{{\Pr ( b_{n,k} )} = {+ 1}}{{\Pr ( b_{n,k} )} = {- 1}} )}}} & \lbrack {{Equation}\mspace{14mu} 4} \rbrack\end{matrix}$

In Equation 4, L(b_(n,k)) denotes an LLR value. “n” may be a value thatis greater than or equal to “1” and less than or equal to “N”, and “k”may be a value that is greater than or equal to “1” and less than orequal to “K”. “K” denotes a number of bits in a QAM symbol defined by aconstellation x:f(b₁, . . . b_(K)). Summing for the constellation may beperformed through all possible combinations of symbols. Theconstellation may include a bit that is equal to “+1” or “−1”. “+1” and“−1” may correspond to initial bit values “0” and “1”, respectively. Asecond summand in Equation 4 is a logarithm ratio of priorprobabilities. Additionally, β_(m) denotes an Euclidian distance valuefor each receiving antenna.

β_(m) may be defined in Equation 5 below. In Equation 5, “m” denotes anumber of receiving antennas, and may have a value that is greater thanor equal to “1” and less than or equal to “N”. β_(m) denotes theEuclidian distance value for an m-th receiving antenna.

$\begin{matrix}{\beta_{m} = {\frac{1}{2\sigma_{\eta}^{2}}{{Y_{m} - {H_{m}X}}}^{2}}} & \lbrack {{Equation}\mspace{14mu} 5} \rbrack\end{matrix}$

A number of possible symbols engaged in calculation of L(b_(k)) may beexponentially increased with regard to a number of transmittingantennas, namely, 2^(M*q) transmitting antennas, and accordingly, theabove-described approach may be unfeasible.

MIMO detection may be performed using a procedure of interferencemitigation. A simplified procedure of the MIMO detection using aconventional interference mitigation may be used. Multiplying a vector Yleft by a matrix Q_(m) with a size of “(M−N+1)×N” may be used. Bymultiplying the left vector Y, all transmitted symbols, namely, symbolstransmitted from all transmitting antennas, may be zero, except a singlesymbol.

As a result of calculation of an LLR, only calculating “(M−N+1)×K”Euclidian distances may be needed. In an example in which a value of Mis identical to a value of N, that is, a number of transmitting antennasis equal to a number of receiving antennas, the proposed approach maymatch a ZF method. The ZF method may exhibit a significant degradationin performance, compared to an approach using the ML detector, and maylose another rather simple method of linear filtration of an MMSE inputsignal.

In the following description, a signal may be iteratively detected anddecoded in communication apparatuses with MIMO channels. A communicationapparatus with a MIMO channel may possess improved receivingcharacteristics. The communication apparatus with the MIMO channel mayprovide lower BERs for decoded information bits, and may have acomplexity that is closer to a simple sum of a complexity of MIMOdetection of MMSE and a complexity of channel decoding used separately.The communication apparatus with the MIMO channel may have a complexitywithout feedback, used in an iterative process.

In the following description, a communication apparatus with a MIMOchannel that achieves the above-described technical result will befurther described.

FIG. 4 illustrates an example of a method of iteratively detecting anddecoding a signal in a communication apparatus with a MIMO channel.Referring to FIG. 4, in operation 410, each of a plurality of receivingantennas receives signals. For example, signals modified by transmittedQAM symbols may be received.

In operation 420, each of the plurality of receiving antennas performdown-conversion of the received signals. The down-conversion may be to azero carrier frequency. The plurality of receiving antennas generate thedown-converted signals through the down-conversion.

In operation 430, an MMSE detector forms samples of the down-convertedsignals. For example, through quantization and digitization, the MMSEdetector may form the samples of the down-converted signals. The samplesof the down-converted signals may be regarded as signals passing throughthe MIMO channel. The samples of the down-converted signals may bedefined based on Equation 6 below.

Y=HX+η  [Equation 6]

In Equation 6, Y denotes an M-dimensional vector of a signal passingthrough the MIMO channel, and X denotes an N-dimensional vector of asignal input to the MIMO channel. η denotes an M-dimensional vector ofnoise samples, and H denotes a MIMO channel matrix with a size of “M×N”.A sampling frequency may be assumed to be equal to a repetitionfrequency of QAM symbols, and accordingly, the vector of the signalinput to the MIMO channel may be a vector of the transmitted QAMsymbols.

In operation 435, the MMSE detector estimates the MIMO channel matrix H,a noise variance σ_(η) ² and a mean signal power σ_(s) ²•, using thesignals received through the MIMO channel.

In operation 440, the MMSE detector estimates the QAM symbols based onthe signals received through the MIMO channel. The estimating of the QAMsymbols may include, for example, detecting the QAM symbols. Operation440 will be further described below with reference to FIG. 5.

In operation 445, a QAM demodulator demodulates the QAM symbolsestimated by the MMSE detector, and estimates a first posteriorprobability of each of encoded bits of the estimated QAM symbols.

The QAM demodulator may estimate a first posterior probability λ_(n,k)based on {tilde over (x)}_(LMS,n) and {tilde over (σ)}_(LMS,n) ². {tildeover (x)}_(LMS,n) and {tilde over (σ)}_(LMS,n) ² may be a thirdmathematical expectation and a third variance, respectively, which willbe described below. The third mathematical expectation and the thirdvariance will be further described below with reference to Equation 13and FIG. 5. The first posterior probability represented by λ_(n,k) maybe calculated by Equation 7 below.

$\begin{matrix}{\lambda_{n,k} = {\ln ( \frac{\sum\limits_{x:{f{({b = 1})}}}{{\exp ( {{- \frac{1}{{\overset{\sim}{\sigma}}_{{LMS},n}^{2}}}{{{\overset{\sim}{x}}_{{LMS},h} - {x( {b_{n,1},b_{n,2},\ldots \mspace{14mu},b_{n,K}} )}}}^{2}} )}{\prod\limits_{t = 1}^{K}{\Pr ( b_{n,t} )}}}}{\sum\limits_{x:{f{({b_{n,k} = {- 1}})}}}{{\exp ( {{- \frac{1}{{\overset{\sim}{\sigma}}_{{LMS},n}^{2}}}{{{\overset{\sim}{x}}_{{LMS},n} - {x( {b_{n,1},b_{n,2},\ldots \mspace{14mu},b_{n,K}} )}}}^{2}} )}{\prod\limits_{t = 1}^{K}{\Pr ( b_{n,t} )}}}} )}} & \lbrack {{Equation}\mspace{14mu} 7} \rbrack\end{matrix}$

In Equation 7, λ_(n,k) denotes the first posterior probability of a k-thencoded bit of an n-th QAM symbol. x(b₁, . . . b_(K)) denotes a tablefunction with “2^(K)” conditions, and may depend on a combination ofencoded bits b_(n,k)ε(k=1, 2, . . . , K, and n=1, 2, . . . , N). x(b₁, .. . b_(K)) may describe a constellation of a transmitted QAM symbol.Pr(b_(n,k)) denotes a prior probability of the k-th encoded bit of then-th QAM symbol that is obtained in a previous iteration cycle. Kdenotes a number of encoded bits in a single QAM symbol.

In operation 450, the QAM demodulator acquires a prior probability ofeach of the encoded bits of the received QAM symbols. For example, theQAM demodulator may acquire a first prior probability λ_(n,k,pr).

The first prior probability λ_(n,k,pr) may be a prior probabilityobtained in the previous iteration cycle, and may be received from asecond module that will be described below. For example, when an initialiteration cycle is performed, the first prior probability λ_(n,k,pr) maybe defined in Equation 8 below.

$\begin{matrix}{\lambda_{n,k,{pr}} = {\ln ( \frac{{\Pr ( b_{n,k} )} = {+ 1}}{{\Pr ( b_{n,k} )} = {- 1}} )}} & \lbrack {{Equation}\mspace{14mu} 8} \rbrack\end{matrix}$

In an example, the first prior probability λ_(n,k,pr) may have a valueobtained by applying a natural logarithm to a value obtained by dividinga probability that the k-th encoded bit of the n-th QAM symbol is “1”,by a probability that the k-th encoded bit of the n-th QAM symbol is“−1”. In this example, “n” may be an integer that is greater than orequal to or greater than “1”, and less than or equal to or less than“N”, and k may be an integer that is greater than or equal to “1”, andless than or equal to “K”. Additionally, N may denote a number of theQAM symbols, and K may denote a number of bits in a QAM symbol. That is,the first prior probability λ_(n,k,pr) may be a logarithm ratio of priorprobabilities of the k-th encoded bit of the n-th QAM symbol.

In operation 455, a first module acquires soft estimates {circumflexover (λ)}_(n,k) of the encoded bits, by removing the first priorprobability λ_(n,k,pr) from the first posterior probability λ_(n,k) ofeach of the encoded bits. For example, the first module may remove priorinformation P_(pr)(b_(n,k)) for each of the encoded bits from the firstposterior probability λ_(n,k), to calculate the soft estimates{circumflex over (λ)}_(n,k). The soft estimates {circumflex over(λ)}_(n,k) may be defined in Equation 9 below. The first module maydiscriminate external information. For example, the external informationmay be acquired by removing the first prior probability from the firstposterior probability. The acquired soft estimates, namely, the externalinformation, may be external probabilities.

{circumflex over (λ)}_(n,k)=λ_(n,k)−λ_(n,k,pr)  [Equation 9]

In Equation 9, {circumflex over (λ)}_(n,k) denotes soft estimates ofposterior probabilities. The first prior probability λ_(n,k,pr) in anext iteration cycle may be acquired by decoding the encoded bits orinformation bits based on channel code parameters. An operation ofacquiring the first prior probability λ_(n,k,pr) in the next iterationcycle will be further described below.

In operation 460, a channel decoder decodes the encoded bits based onthe soft estimates {circumflex over (λ)}_(n,k), and acquires an improvedposterior probability {circumflex over (λ)}_(n,k) of each of the encodedbits. The channel decoder may receive the encoded bits from the QAMdemodulator. The improved posterior probability {circumflex over(λ)}_(n,k) may be acquired by estimating a posterior probability of eachof the encoded bits. The improved posterior probability {circumflex over(λ)}_(n,k) may be an improved logarithm ratio of posterior probabilitiesof each of the encoded bits. The improved posterior probability{circumflex over (λ)}_(n,k) may be, for example, an improved softestimate of the soft estimates {circumflex over (λ)}_(n,k). An output ofthe channel decoder may include the improved posterior probability ofeach of the encoded bits.

The channel decoder may decode the information bits, using the estimatedposterior probability of each of the encoded bits. The channel decodermay estimate a posterior probability of each of the information bitsbased on parameters of a channel code. To estimate the posteriorprobability of each of the information bits, the channel decoder maydecode the encoded bits. An improved posterior probability of each ofthe information bits may be, for example, an improved posteriorprobability of each of the encoded bits. Additionally, the channeldecoder may acquire the estimated posterior probability of each of theencoded bits with improved accuracy. The channel decoder may output softbits. The improved posterior probability {circumflex over (λ)}_(n,k) maybe soft estimates of the output soft bits.

A deinterleaver may deinterleave the encoded bits, before the encodedbits are input to the channel decoder. An interleaver may interleave thesoft bits output from the channel decoder. The interleaved soft bits maybe input to the second module and a remodulator.

In operation 465, the second module acquires a second prior probabilityλ_(n,k,pr) based on the improved posterior probability {circumflex over(λ)}_(n,k) of each of the encoded bits. The second module acquires thesecond prior probability λ_(n,k,pr) by removing the soft estimates{circumflex over (λ)}_(n,k) from the improved posterior probability. Thesecond module may discriminate external information. For example, theexternal information may be acquired by removing the soft estimates{circumflex over (λ)}_(n,k) from the improved posterior probability. Thesecond prior probability, namely, the external information, may beexternal probabilities. The second prior probability λ_(n,k,pr) acquiredby the second module may be a first prior probability in the nextiteration cycle. The second prior probability λ_(n,k,pr) may be definedin Equation 10 below.

$\begin{matrix}{{{\Pr ( b_{n,k} )} = \frac{^{b_{n,k}\frac{\lambda_{n,k,{pr}}}{2}}}{^{\frac{\lambda_{n,k,{pr}}}{2}} + ^{- \frac{\lambda_{n,k,{pr}}}{2}}}},{\lambda_{n,k,{pr}} = {{\overset{\sim}{\lambda}}_{n,k} - {\overset{\Cap}{\lambda}}_{n,k}}}} & \lbrack {{Equation}\mspace{14mu} 10} \rbrack\end{matrix}$

The second module may define an estimate of a posterior probability ofeach of encoded bits in the next iteration cycle, based on the secondprior probability λ_(n,k,pr). In an example, the second priorprobability λ_(n,k,pr) may be input to the QAM demodulator and the firstmodule in the next iteration cycle. In this example, when an improvedposterior probability of each of encoded bits is estimated at least onetime, the first module may use the input second prior probabilityλ_(n,k,pr) as a first prior probability of each of the encoded bits inthe next iteration cycle, and may acquire an improved posteriorprobability of each of the encoded bits in the next iteration cycle.

In operation 470, the remodulator acquires a first mathematicalexpectation {circumflex over (x)}_(n) and a first variance σ_(n) ²,based on the improved posterior probability {tilde over (λ)}_(n,k) ofeach of the encoded bits. The first mathematical expectation {circumflexover (x)}_(n) may be acquired, by acquiring a weighted sum of values ofan QAM constellation. The first mathematical expectation {circumflexover (x)}_(n) may be, for example, a mathematical expectation of asymbol vector of an QAM symbol input to the remodulator. The improvedposterior probability of each of the encoded bits is output from thechannel decoder. The first mathematical expectation {circumflex over(x)}_(n) may be defined in Equation 11 below.

$\begin{matrix}{{\hat{x}}_{n} = {\sum\limits_{b_{n,1},b_{n,2},\mspace{11mu} {{\ldots \mspace{14mu} b_{n,K}} \in {\{{{- 1};1}\}}}}{{x( {b_{n,1},b_{n,2},\ldots \mspace{14mu},b_{n,K}} )}{\overset{\sim}{P}( {b_{n,1},b_{n,2},\ldots \mspace{14mu},b_{n,K}} )}}}} & \lbrack {{Equation}\mspace{14mu} 11} \rbrack\end{matrix}$

In Equation 11, {tilde over (P)}(b_(n,1), b_(n,2), . . . , b_(n,K))denotes weights. {tilde over (P)}(b_(n,1), b_(n,2), . . . , b_(n,K)) maybe defined in Equation 12 below. {tilde over (P)}(b_(n,1), b_(n,2), . .. , b_(n,K)) may be defined by the improved posterior probability ofeach of the encoded bits.

$\begin{matrix}{{\overset{\sim}{P}( {b_{n,1},b_{n,2},\ldots \mspace{14mu},b_{n,K}} )} = {\prod\limits_{k = 1}^{K}\frac{^{b_{n,k}\frac{{\overset{\sim}{\lambda}}_{n,k}}{2}}}{^{\frac{{\overset{\sim}{\lambda}}_{n,k}}{2}} + ^{- \frac{{\overset{\sim}{\lambda}}_{n,k}}{2}}}}} & \lbrack {{Equation}\mspace{14mu} 12} \rbrack\end{matrix}$

The first variance σ_(n) ² may be defined in Equation 13 below.

$\begin{matrix}{\sigma_{n}^{2} = {{\sum\limits_{b_{n,1},b_{n,2},\mspace{11mu} \ldots \mspace{14mu},{b_{n,K} \in {\{{{- 1};1}\}}}}{{{x( {b_{n,1},b_{n,2},\ldots \mspace{14mu},b_{n,K}} )}}^{2}{\overset{\sim}{P}( {b_{n,1},b_{n,2},\ldots \mspace{14mu},b_{n,K}} )}}} - {{\hat{x}}_{n}}^{2}}} & \lbrack {{Equation}\mspace{14mu} 13} \rbrack\end{matrix}$

A third module of the communication apparatus may output priorinformation to an auxiliary input of the MMSE detector. The priorinformation output from the third module may be needed to generate newestimates of posterior probabilities of encoded bits and informationbits to be used in the next iteration cycle. The prior informationoutput from the third module may include, for example, a secondmathematical expectation {tilde over (x)}_(pr,n) and a second variance{tilde over (σ)}_(pr,n) ² that will be described below.

In operation 475, the third module compares the first variance σ_(n) ²with a third variance {tilde over (σ)}_(LMS,n) ² acquired afterestimation of QAM symbols of the previous iteration cycle. Based on aresult of the comparing, the third module may correct the priorinformation input to the MMSE detector. A third mathematical expectation{tilde over (x)}_(LMS,n) may be acquired after the estimation of the QAMsymbols of the previous iteration cycle.

In an example in which the first variance σ_(n) ² is less than the thirdvariance {tilde over (σ)}_(LMS,n) ², the third module may acquire thesecond mathematical expectation {circumflex over (x)}_(pr,n) and thesecond variance {tilde over (σ)}_(pr,n) ², by performing a correction ofEquation 14.

$\begin{matrix}{{{\overset{\sim}{x}}_{{pr},n} = {{{- \frac{\sigma_{n}^{2}}{{\overset{\sim}{\sigma}}_{{LMS},n}^{2} - \sigma_{n}^{2}}}{\overset{\sim}{x}}_{{LMS},n}} + {\frac{{\overset{\sim}{\sigma}}_{{LMS},n}^{2}}{{\overset{\sim}{\sigma}}_{{LMS},n}^{2} - \sigma_{n}^{2}}{\hat{x}}_{n}}}}{{\overset{\sim}{\sigma}}_{{pr},n}^{2} = \frac{\sigma_{n}^{2}{\overset{\sim}{\sigma}}_{{LMS},n}^{2}}{{\overset{\sim}{\sigma}}_{{LMS},n}^{2} - \sigma_{n}^{2}}}} & \lbrack {{Equation}\mspace{14mu} 14} \rbrack\end{matrix}$

For example, the third module may acquire the second mathematicalexpectation {tilde over (x)}_(pr,n) based on the first mathematicalexpectation {circumflex over (x)}_(n), the first variance σ_(n) ², thethird mathematical expectation {tilde over (X)}_(LMS,n), and the thirdvariance {tilde over (σ)}_(LMS,n) ². Additionally, the third module mayacquire the second variance {tilde over (σ)}_(pr,n) ² based on the firstvariance σ_(n) ² and the third variance {tilde over (σ)}_(pr,n) ².

In another example in which the first variance σ_(n) ² is greater thanor equal to the third variance {tilde over (σ)}_(LMS,n) ², the thirdmodule may acquire the second mathematical expectation {tilde over(x)}_(pr,n) and second variance {tilde over (σ)}_(pr,n) ², by performinga correction of Equation 15.

{tilde over (x)} _(pr,n) = x _(pr,n)

{tilde over (σ)}_(pr,n) ²=σ_(pr,n) ²  [Equation 15]

In Equation 15, x _(pr,n) and σ_(pr,n) ² denote a second mathematicalexpectation and a second variance, respectively, which are acquired inthe previous iteration cycle. In other words, the second mathematicalexpectation x _(pr,n) and the second variance σ_(pr,n) ² that areacquired in the previous iteration cycle may be identical to the secondmathematical expectation {tilde over (x)}_(pr,n) and the second variance{tilde over (σ)}_(pr,n) ², respectively, which are acquired in a currentiteration cycle.

In operation 480, the third module generates new input parameters of theMMSE detector, based on a result of operation 475. The third module maygenerate the new input parameters for estimation of QAM symbols in thenext iteration cycle, based on the result of operation 475. The newinput parameters generated by the third module may include the secondmathematical expectation {tilde over (x)}_(pr,n) and the second variance{tilde over (σ)}_(pr,n) ².

The new input parameters may include new mathematical expectations{tilde over (X)}_(pr) and a new prior diagonal correlation matrix {tildeover (V)}_(pr). The new mathematical expectations {tilde over (X)}_(pr)may be generated based on the second mathematical expectation {tildeover (x)}_(pr,n). The new prior diagonal correlation matrix {tilde over(V)}_(pr) may be generated based on the second variance {tilde over(σ)}_(pr,n) ². For example, the second mathematical expectation {tildeover (x)}_(pr,n) and the second variance {tilde over (σ)}_(pr,n) ² forall “n” may be used as vectors to generate the new mathematicalexpectations {tilde over (X)}_(pr) and the new prior diagonalcorrelation matrix {tilde over (V)}_(pr), respectively. In this example,“n” may be an integer that is greater than or equal to “1” and less thanor equal to “N”. The new mathematical expectations {tilde over (X)}_(pr)may be a matrix including vectors of prior mathematical expectations{tilde over (x)}_(pr,n) for all “n”, and the new prior diagonalcorrelation matrix {tilde over (V)}_(pr) may be a matrix includingvectors of prior variances {tilde over (σ)}_(pr,n) ² for all “n”.

The second mathematical expectation {tilde over (x)}_(pr,n) and thesecond variance {tilde over (σ)}_(pr,n) ² may be used, as priorinformation, to estimate QAM symbols in the next iteration cycle. TheMMSE detector may acquire the third mathematical expectation {tilde over(x)}_(LMS,n) and the third variance {tilde over (σ)}_(LMS,n) ² based onthe second mathematical expectation {tilde over (x)}_(pr,n) and thesecond variance {tilde over (σ)}_(pr,n) ², as prior information, and mayestimate QAM symbols. The estimating of QAM symbols will be furtherdescribed with reference to FIG. 5.

In operation 485, a hard-decision estimator forms a sequence of hardestimates of information bits, based on the improved posteriorprobability {tilde over (λ)}_(n,k) acquired by the channel decoder. Theformed sequence may be output data. Operation 480 may be performed afterall iteration cycles are completed, and the sequence of the hardestimates that is formed by the hard-decision estimator may be, forexample, a final sequence of the hard estimates of the information bits.By forming the final sequence, the hard-decision estimator may restoreoriginal data. An estimate of a posterior probability of each of theinformation bits may exist in a main output of the channel decoder.

The above-described method of FIG. 4 may be used in a communicationapparatus with a MIMO channel. The transmission apparatus with the MIMOchannel, and the reception apparatus with the MIMO channel, have beendescribed above with reference to FIGS. 1 through 3, and the method ofFIG. 4 may be applied to the transmission apparatus and the receptionapparatus.

FIG. 5 illustrates an example of operation 440 of estimating QAM symbolsin the method of FIG. 4. Operation 440 of FIG. 4 will be furtherdescribed with reference to FIG. 5. The MMSE detector estimates QAMsymbols through operations 510 and 520 of FIG. 5 that will be describedbelow. Operation 440 may include operations 510 and 520.

In operation 510, the MMSE detector acquires an MMSE estimationmathematical expectation {tilde over (x)}_(MMSE,n) and an MMSEestimation variance σ_(MMSE,n) ² of a symbol vector of QAM symbolsreceived at the MMSE detector, by applying an MMSE linear filter to thesymbol vector. The MMSE linear filter may be defined in Equation 16below.

$\begin{matrix}{G_{MMSE} = {V_{pr}{H^{\prime}( {{{HV}_{pr}H^{\prime}} + {\frac{\sigma_{\eta}^{2}}{\sigma_{s}^{2}}I}} )}^{- 1}}} & \lbrack {{Equation}\mspace{14mu} 16} \rbrack\end{matrix}$

In Equation 16, V_(pr) denotes a diagonal matrix. V_(pr) may describe aprior variance of QAM symbols that are normalized by the mean signalpower σ_(s) ²• and transmitted.

By the MMSE linear filter, an MMSE estimation vector {tilde over(X)}_(MMSE) may be defined in Equation 17 below.

{tilde over (X)} _(MMSE) = X _(pr) +G _(MMSE)(Y−H X _(pr))  [Equation17]

In Equation 17, X _(pr) denotes an N-dimensional vector of transmittedQAM symbols. X _(pr) may be an N-dimensional vector of priormathematical expectations of the transmitted QAM symbols. For eachcomponent {tilde over (x)}_(MMSE,n) (n=1, 2, . . . , N) of the MMSEestimation vector {tilde over (X)}_(MMSE), the MMSE estimation varianceσ_(MMSE,n) ² may be acquired. {tilde over (x)}_(MMSE,n) may be an MMSEestimation mathematical expectation of a symbol vector of each of QAMsymbols. σ_(MMSE,n) ² may be a variance of an MMSE estimation error.σ_(MMSE,n) ² may be identical to corresponding diagonal elements of acorrelation matrix V_(MMSE). The correlation matrix V_(MMSE) may bedefined in Equation 18 below.

V _(MMSE) =V _(pr) −G _(MMSE) HV _(pr)  [Equation 18]

In operation 520, the MMSE detector acquires the third mathematicalexpectation {tilde over (x)}_(LMS,n) and the third variance {tilde over(σ)}_(LMS,n) ² to be used in the next iteration cycle. The MMSE detectormay perform linear transformation of an n-th component {tilde over(x)}_(MMSE,n) of the MMSE estimation vector {tilde over (X)}_(MMSE), anda prior mathematical expectation x _(pr,n) corresponding to the n-thcomponent {tilde over (x)}_(MMSE,n). “n” may be an integer that isgreater than or equal to “1”, and less than or equal to “N”. The MMSEdetector may correct the linear transformation, and may generate a newlinear estimate {tilde over (x)}_(LMS,n) of a transmitted QAM symbol,and a variance {tilde over (σ)}_(LMS,n) ² of an error for the new linearestimate. The new linear estimate {tilde over (x)}_(LMS,n) may be thethird mathematical expectation {tilde over (x)}_(LMS,n) that may be usedin the next iteration cycle, and the variance {tilde over (σ)}_(LMS,n)may be the third variance that may be used in the next iteration cycle.

{tilde over (x)}_(LMS,n) ² and {tilde over (σ)}_(LMS,n) may be obtainedby Equation 19 below. {tilde over (x)}_(LMS,n) and {tilde over(σ)}_(LMS,n) ² may be the third mathematical expectation and the thirdvariance, respectively.

$\begin{matrix}{{{\overset{\sim}{x}}_{{LMS},n} = {{{- \frac{\sigma_{{MMSE},n}^{2}}{\sigma_{{pr},n}^{2} - \sigma_{{MMSE},n}^{2}}}{\overset{\_}{x}}_{{pr},n}} + {\frac{\sigma_{{pr},n}^{2}}{\sigma_{{pr},n}^{2} - \sigma_{{MMSE},n}^{2}}{\overset{\sim}{x}}_{{MMSE},n}}}}\mspace{79mu} {{\overset{\sim}{\sigma}}_{{LMS},n}^{2} = \frac{\sigma_{{pr},n}^{2}\sigma_{{MMSE},n}^{2}}{\sigma_{{pr},n}^{2} - \sigma_{{MMSE},n}^{2}}}} & \lbrack {{Equation}\mspace{14mu} 19} \rbrack\end{matrix}$

In Equation 19, x _(pr,n) denotes a prior estimate of a mathematicalexpectation of an n-th transmitted QAM symbol, and σ_(pr,n) ² denotes aprior estimate of a variance of the n-th transmitted QAM symbol. x_(pr,n) and σ_(pr,n) ² may be the second mathematical expectation andthe second variance, respectively, which are acquired in the previousiteration cycle and are described above with reference to FIG. 4. Forexample, the MMSE detector may acquire the third mathematicalexpectation {tilde over (x)}_(LMS,n) to be used in the next iterationcycle, based on the second mathematical expectation x _(pr,n) in theprevious iteration cycle, the second variance σ_(pr,n) ² in the previousiteration cycle, the MMSE estimation mathematical expectation {tildeover (x)}_(MMSE,n), and the MMSE estimation variance {tilde over(σ)}_(MMSE,n) ². Additionally, the MMSE detector may acquire the thirdvariance σ_(LMS,n) ² to be used in the next iteration cycle, based onthe second variance {tilde over (σ)}_(pr,n) ² and the MMSE estimationvariance σ_(MMSE,n) ².

When {tilde over (x)}_(LMS,n) and {tilde over (σ)}_(LMS,n) ² areacquired, a QAM symbol in the next iteration cycle may be estimated. Theacquired {tilde over (x)}_(LMS,n) and {tilde over (σ)}_(LMS,n) ² may beinput to the QAM demodulator in the next iteration cycle. As describedabove with reference to FIG. 4, the QAM demodulator may estimate thefirst posterior probability λ_(n,k), based on the input {tilde over(x)}_(LMS,n) and {tilde over (σ)}_(LMS,n) ². When the third mathematicalexpectation {tilde over (σ)}_(LMS,n) and the third variance {tilde over(σ)}_(LMS,n) ² are initially acquired by the MMSE detector, the secondmathematical expectation x _(pr,n) in the previous iteration cycle maybe set to “0”, and the second variance σ_(pr,n) ² in the previousiteration cycle may be set to a unit variance. In other words, in aninitial iteration cycle, a vector with a zero mathematical expectationand a unit variance may be used as a prior estimate of a vector of areceived QAM symbol. Technical information described above withreference to FIGS. 1 through 4 may be applied without a change, andaccordingly, further description is omitted herein.

FIG. 6 illustrates an example of a communication apparatus 600 with aMIMO channel and configured to iteratively detect and decode a signal.Referring to FIG. 6, the communication apparatus 600 with the MIMOchannel includes an MMSE detector 601, a QAM demodulator 603, a firstmodule 605, a deinterleaver 607, a channel decoder 609, an interleaver611, a second module 613, a remodulator 615, a third module 617, and ahard-decision estimator 619.

Elements with the same names, which are described above in FIGS. 4 and5, as the MMSE detector 601, the QAM demodulator 603, the first module605, the deinterleaver 607, the channel decoder 609, the interleaver611, the second module 613, the remodulator 615, the third module 617,and the hard-decision estimator 619 may be applied to the communicationapparatus 600. For example, operations 410 through 485 of FIG. 4 may beperformed by the communication apparatus 600.

The communication apparatus 600 may further include a plurality ofreceiving antennas, although not illustrated. Each of the plurality ofreceiving antennas may receive signals modulated by transmitted QAMsymbols. The plurality of receiving antennas may perform down-conversionof the received signals, respectively. The down-conversion may be to azero carrier frequency. The plurality of receiving antennas may generatedown-converted signals through the down-conversion.

The signals received, amplified and down-converted by the plurality ofreceiving antennas are input to the MMSE detector 601. In FIG. 6, y_(l),y_(m), and y_(M) represent the down-converted signals. Thedown-converted signals may be represented by Y in Equation 6. Y maydenote a vector of the signals received by the plurality of receivingantennas. Additionally, H, σ_(η) ² and σ_(s) ²• are input to the MMSEdetector 601. H denotes components of a channel matrix.

Prior information of a symbol vector of a received QAM symbol may alsobe input to the MMSE detector 601. The prior information includes amathematical expectation X _(pr) and a variance V_(pr) of components ofthe symbol vector that are received in a previous iteration. Themathematical expectation X _(pr) and variance V_(pr) may be generatedbased on a second mathematical expectation {tilde over (x)}_(pr,n) and asecond variance {tilde over (σ)}_(pr,n) ² output from the third module617.

The MMSE detector 601 estimates received QAM symbols through operations510 and 520 of FIG. 5. To estimate the QAM symbols, the MMSE linearfilter defined by Equation 16 may be applied.

In detail, the MMSE detector 601 generates a mathematical expectation ofan estimated symbol vector that is represented by {tilde over(x)}_(LMS,n) and a variance of an error of the mathematical expectation{tilde over (x)}_(LMS,n) that is represented by {tilde over (σ)}_(LMS,n)². {tilde over (x)}_(LMS,n) and {tilde over (σ)}_(LMS,n) ² may be thethird mathematical expectation and the third variance that are describedabove with reference to FIGS. 4 and 5.

The mathematical expectation {tilde over (x)}_(LMS,n) and variance{tilde over (σ)}_(LMS,n) ² are input to the QAM demodulator 603.Additionally, {tilde over (x)}_(LMS,n) and {tilde over (σ)}_(LMS,n) ²are input to the third module 617. The third module 617 acquires thesecond mathematical expectation {tilde over (x)}_(pr,n) and the secondvariance {tilde over (σ)}_(pr,n) ², based on {tilde over (x)}_(LMS,n)and {tilde over (σ)}_(LMS,n) ². {tilde over (x)}_(LMS,n) and {tilde over(σ)}_(LMS,n) ² may be defined by Equation 19 that is described above.

The QAM demodulator 603 demodulates the estimated QAM symbols, andestimates a first posterior probability for each of encoded bits of therespective demodulated QAM symbols. The first posterior probability isrepresented by λ_(n,k). λ_(n,k) may be defined by Equation 7 that isdescribed above.

Additionally, the QAM demodulator 603 acquires a first prior probabilityfor each of the encoded bits of the respective received QAM symbols. Thefirst prior probability λ_(n,k,pr) are acquired from a previousiteration cycle, are received from the second module 613, and may bedefined by Equation 8 that is described above.

The first module 605 removes the first prior probability λ_(n,k,pr) ofeach of the encoded bits from the first posterior probability λ_(n,k) ofeach of the encoded bits, to generate soft estimates {circumflex over(λ)}_(n,k) of the encoded bits. Accordingly, external informationobtained in the MMSE detector 601 may be generated.

The deinterleaver 607 deinterleaves the encoded bits, before the encodedbits are input to the channel decoder 609.

The channel decoder 609 performs the above-described decoding inaccordance with channel code parameters. The channel decoder 609 may usea plurality of decoding methods, for example, a Viterbi method, amaximum posterior probability (MAP) method, a turbo-decoding method, abelieve propagation method, and/or other decoding methods known to oneof ordinary skill in the art. To estimate a posterior probability, a MAPdecoder and/or a Log-MAP decoder may be used. The decoding scheme may beselected by at least one factor, for example, a type of a used codeand/or an efficiency of applied algorithms. The efficiency may be inproportion to a noise immunity at an acceptable implementationcomplexity.

The selected decoding method in the above-described examples may not beessential. For example, various decoding methods enabling apredetermined estimation to be more accurately performed may be applied.The predetermined estimation may include, for example, an estimation ofa probability of information and/or an estimation of a probability ofencoded bits.

The channel decoder 609 generates an estimate of a posterior probabilityof each of information bits. The channel decoder 609 performs thedecoding, and estimates a probability of each of the encoded bits basedon the posterior probability of each of the information bits. Estimationof posterior information of the encoded bits after the decoding may becompleted using an alternative way, namely, re-encoding.

The channel decoder 609 acquires an improved posterior probability ofeach of the encoded bits. For example, the channel decoder 609 maydecode the encoded bits based on the soft estimates {circumflex over(λ)}_(n,k) of the encoded bits, and may acquire the improved posteriorprobability {tilde over (λ)}_(n,k) of each of the encoded bits. A firstoutput of the channel decoder 609 includes the acquired improvedposterior probability {tilde over (λ)}_(n,k), which may be called softbits. The first output may include, for example, improved soft estimatesof the encoded bits.

The interleaver 611 interleaves the soft bits included in the firstoutput. The improved posterior probability {tilde over (λ)}_(n,k) ofeach of the encoded bits in the first output is input to the secondmodule 613. The second module 613 acquires predetermined informationbased on the improved posterior probability {tilde over (λ)}_(n,k) andthe soft estimates {circumflex over (λ)}_(n,k). The acquiredpredetermined information includes the second prior probabilityλ_(n,k,pr). The second prior probability λ_(n,k,pr) is input to the QAMdemodulator 603 and the first module 605. In an example in which theimproved posterior probability is estimated at least one time, the firstmodule 605 uses the input second prior probability as a first priorprobability. The second prior probability λ_(n,k,pr) input to the QAMdemodulator 603 is used by the QAM demodulator 603 in a next iterationcycle.

The first output passing through the interleaver 611 is also input tothe remodulator 615. The remodulator 615 estimates a vector of thereceived QAM symbol based on the improved posterior probability {tildeover (λ)}_(n,k). The remodulator 615 acquires a first mathematicalexpectation {circumflex over (x)}_(n) and a first variance σ_(n) ² ofQAM symbols. The first mathematical expectation {circumflex over(x)}_(n) and first variance σ_(n) ² may be defined by Equations 11through 13. {circumflex over (x)}_(n) may denote a mathematicalexpectation of each vector of the QAM symbols, and σ_(n) ² may denote avariance of an error of {circumflex over (x)}_(n).

The first mathematical expectation {circumflex over (x)}_(n) and firstvariance σ_(n) ² are input to the third module 617. The third module 617corrects prior information. The third module 617 receives estimates of avector of a QAM symbol obtained by the MMSE detector 601 in the previousiteration cycle. That is, the third module 617 receives {tilde over(x)}_(LMS,n) and {tilde over (σ)}_(LMS,n) ² of the previous iterationcycle from the MMSE detector 601. The third module 617 compares thefirst variance σ_(n) ² with {tilde over (σ)}_(LMS,n) ² of the previousiteration cycle. The third module 617 generates new input parameters ofthe MMSE detector 601 based on a result of the comparing. The generatedinput parameters includes the second mathematical expectation {tildeover (x)}_(pr,n) and second variance {tilde over (σ)}_(pr,n) ².

The third module 617 corrects the vector of the QAM symbol, and outputsa prior estimate. The prior estimate includes {tilde over (x)}_(pr,n)and {tilde over (σ)}_(LMS,n) ². The MMSE detector 601 may use the outputprior estimate as prior information, namely a prior estimate, in thenext iteration cycle.

A second output of the channel decoder 609 includes the improved softestimates of the encoded bits. The improved soft estimates are calledthe soft bits. The soft bits are input to the hard-decision estimator619. The hard-decision estimator 619 performs hard estimation of thesoft bits. The hard-decision estimator 619 may form a sequence of hardestimates of information bits, as output data, based on the improvedposterior probability acquired by the channel decoder 609. Wheniteration cycles are performed a plurality of number of times, thesecond output may be input to the hard-decision estimator 619. Forexample, a final sequence may be formed by the hard-decision estimator619. By forming the final sequence, the hard-decision estimator 619 mayrestore original data.

By performing iteration cycles a plurality of number of times,reliability of data received at the communication apparatus 600 may beenhanced. For example, when iteration cycles are performed a pluralityof number of times, a final hard estimate of each of information bitsmay be matched to a transmitted sequence with a further enhancedprobability.

In a process of iteratively decoding and detecting a received signal,transformation of digital representation of the process may be describedwith reference to Equations 1 through 19 that are described above.

In the above-described examples, channel decoding may be fulfilled byiteration of at least one cycle. A single external iteration cycle mayinclude joint detection and decoding. The joint detection and decodingmay include at least one internal cycle of an operation of the channeldecoder 609.

Additionally, in the above-described examples, a bit interleavingprocedure may be applied after channel encoding during initial signalgeneration. For example, QAM symbols of encoded bits that areinterleaved may be demodulated by the QAM demodulator 603. A bitinterleaving procedure by the interleaver 611 may be applied afterchannel decoding performed by the channel decoder 609 during receivingof a sequence of probability estimates of demodulated bits. The sequenceof the probability estimates of the demodulated bits may bedeinterleaved by passing through the deinterleaver 607, prior toinputting to the channel decoder 609. A sequence of probabilityestimates of encoded bits after decoding performed by the channeldecoder 609, may be used in a feedback line. The sequence of theprobability estimates of the encoded bits may be interleaved by passingthrough the interleaver 611 by turn. An interleaving procedure may beperformed to generate prior estimates. The sequence passing through theinterleaver 611 may be input to the MMSE detector 601 and the QAMdemodulator 603 in the next iteration cycle.

Encoded bits may be divided into systematic bits and parity check bitsduring initial signal generation. Accordingly, a sequence of estimatesof probabilities of bits generated by a decoding procedure by thechannel decoder 409 may include only systematic bits. The remodulator615 may process a re-encoding procedure of a sequence of estimates ofprobabilities of systematic bits after decoding. By the re-encodingprocedure, the remodulator 615 may add parity check bits to estimates,and may restore a full sequence of encoded bits. The restored fullsequence may be used by the MMSE detector 601 and the QAM demodulator603 in the next iteration cycle.

In the re-encoding procedure, probabilities of verifying bits may beestimated, based on probabilities of systematic bits and in accordancewith parameters of a channel code. The verifying bits may be paritycheck bits. The re-encoding procedure performed by the remodulator 615may include adding, to estimates, values of a probability of paritycheck bits characterized by zero LLR.

FIG. 7 illustrates an example of results of iterative detection anddecoding of a signal. The results of FIG. 7 may be obtained based on along term evolution (LTE) standard model. In FIG. 7, a MIMOconfiguration of “4×4”, 16 QAM, and a channel of EPA-5 are used.Additionally, a code rate is set to “½”, namely, 3,640,000 bits. In agraph of FIG. 7, an x-axis represents a signal-to-noise ratio (SNR) indecibels (dB), and a y-axis represents a BER performance.

The results of iterative detection and decoding are illustrated in FIG.7. In FIG. 7, “MMSE” represents a result obtained by detecting anddecoding a signal, using an MMSE detector as a MIMO detector, and “ML”represents a result obtained by detecting and decoding a signal, usingan ML detector as a MIMO detector. Additionally, “TP FB” represents aresult obtained by detecting and decoding a signal, using an iterativedetection and demodulating unit according to the above-describedexamples. As illustrated in the graph of FIG. 7, a method and anapparatus according to the above-described examples may have asignificantly better effect than a method or an apparatus with the MMSEdetector.

FIG. 8 illustrates another example of results of iterative detection anddecoding of a signal. The results of FIG. 8 may be obtained based on anLTE standard model. In FIG. 8, a MIMO configuration of “4×4”, 16 QAM,and a channel of EPA-5 are used. Additionally, a code rate is set to“¾”, namely, 5,432,000 bits. In a graph of FIG. 8, an x-axis representsan SNR in dB, and a y-axis represents a BER performance.

The results of iterative detection and decoding are illustrated in FIG.8. In FIG. 8, “MMSE” represents a result obtained by detecting anddecoding a signal, using an MMSE detector as a MIMO detector, and “ML”represents a result obtained by detecting and decoding a signal, usingan ML detector as a MIMO detector. Additionally, “TP FB” represents aresult obtained by detecting and decoding a signal, using an iterativedetection and demodulating unit according to the above-describedexamples. As illustrated in the graph of FIG. 8, a method and anapparatus according to the above-described examples may have asignificantly better effect than a method or an apparatus with the MMSEdetector.

In joint iterative detection and decoding, only two external cycles ofiterative detection and decoding may be used while a channel decoderfulfills decoding using internal iterative cycles. In decoding usinginternal cycles, a turbo code may be decoded. Accordingly, after a firstcycle of an operation of the MMSE detector, a channel decoder mayperform two internal cycles of decoding. When the first cycle iscompleted, another cycle of detection may be performed using priorinformation obtained from the channel decoder. When a second cycle iscompleted, the channel decoder may further perform four internal cyclesof decoding. Accordingly, a total number of internal cycles of anoperation of the channel decoder may be six. The same number of cyclesof the operation of the channel decoder may be used in conventionalschemes of consequent detecting and decoding. In other words, acomplexity and delay may increase only due to an additional cycle of aMIMO detection. The above increase in the complexity and delay may beinsignificant in view of a comparative simplicity of a MIMO detectionmethod based on an MMSE filtration.

The various units, modules, elements, and methods described above may beimplemented using one or more hardware components, one or more softwarecomponents, or a combination of one or more hardware components and oneor more software components.

A hardware component may be, for example, a physical device thatphysically performs one or more operations, but is not limited thereto.Examples of hardware components include microphones, amplifiers,low-pass filters, high-pass filters, band-pass filters,analog-to-digital converters, digital-to-analog converters, andprocessing devices.

A software component may be implemented, for example, by a processingdevice controlled by software or instructions to perform one or moreoperations, but is not limited thereto. A computer, controller, or othercontrol device may cause the processing device to run the software orexecute the instructions. One software component may be implemented byone processing device, or two or more software components may beimplemented by one processing device, or one software component may beimplemented by two or more processing devices, or two or more softwarecomponents may be implemented by two or more processing devices.

A processing device may be implemented using one or more general-purposeor special-purpose computers, such as, for example, a processor, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a field-programmable array, a programmable logic unit, amicroprocessor, or any other device capable of running software orexecuting instructions. The processing device may run an operatingsystem (OS), and may run one or more software applications that operateunder the OS. The processing device may access, store, manipulate,process, and create data when running the software or executing theinstructions. For simplicity, the singular term “processing device” maybe used in the description, but one of ordinary skill in the art willappreciate that a processing device may include multiple processingelements and multiple types of processing elements. For example, aprocessing device may include one or more processors, or one or moreprocessors and one or more controllers. In addition, differentprocessing configurations are possible, such as parallel processors ormulti-core processors.

A processing device configured to implement a software component toperform an operation A may include a processor programmed to runsoftware or execute instructions to control the processor to performoperation A. In addition, a processing device configured to implement asoftware component to perform an operation A, an operation B, and anoperation C may have various configurations, such as, for example, aprocessor configured to implement a software component to performoperations A, B, and C; a first processor configured to implement asoftware component to perform operation A, and a second processorconfigured to implement a software component to perform operations B andC; a first processor configured to implement a software component toperform operations A and B, and a second processor configured toimplement a software component to perform operation C; a first processorconfigured to implement a software component to perform operation A, asecond processor configured to implement a software component to performoperation B, and a third processor configured to implement a softwarecomponent to perform operation C; a first processor configured toimplement a software component to perform operations A, B, and C, and asecond processor configured to implement a software component to performoperations A, B, and C, or any other configuration of one or moreprocessors each implementing one or more of operations A, B, and C.Although these examples refer to three operations A, B, C, the number ofoperations that may implemented is not limited to three, but may be anynumber of operations required to achieve a desired result or perform adesired task.

Software or instructions for controlling a processing device toimplement a software component may include a computer program, a pieceof code, an instruction, or some combination thereof, for independentlyor collectively instructing or configuring the processing device toperform one or more desired operations. The software or instructions mayinclude machine code that may be directly executed by the processingdevice, such as machine code produced by a compiler, and/or higher-levelcode that may be executed by the processing device using an interpreter.The software or instructions and any associated data, data files, anddata structures may be embodied permanently or temporarily in any typeof machine, component, physical or virtual equipment, computer storagemedium or device, or a propagated signal wave capable of providinginstructions or data to or being interpreted by the processing device.The software or instructions and any associated data, data files, anddata structures also may be distributed over network-coupled computersystems so that the software or instructions and any associated data,data files, and data structures are stored and executed in a distributedfashion.

For example, the software or instructions and any associated data, datafiles, and data structures may be recorded, stored, or fixed in one ormore non-transitory computer-readable storage media. A non-transitorycomputer-readable storage medium may be any data storage device that iscapable of storing the software or instructions and any associated data,data files, and data structures so that they can be read by a computersystem or processing device. Examples of a non-transitorycomputer-readable storage medium include read-only memory (ROM),random-access memory (RAM), flash memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs,CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs,BD-Rs, BD-R LTHs, BD-REs, magnetic tapes, floppy disks, magneto-opticaldata storage devices, optical data storage devices, hard disks,solid-state disks, or any other non-transitory computer-readable storagemedium known to one of ordinary skill in the art.

Functional programs, codes, and code segments for implementing theexamples disclosed herein can be easily constructed by a programmerskilled in the art to which the examples pertain based on the drawingsand their corresponding descriptions as provided herein.

While this disclosure includes specific examples, it will be apparent toone of ordinary skill in the art that various changes in form anddetails may be made in these examples without departing from the spiritand scope of the claims and their equivalents. The examples describedherein are to be considered in a descriptive sense only, and not forpurposes of limitation. Descriptions of features or aspects in eachexample are to be considered as being applicable to similar features oraspects in other examples. Suitable results may be achieved if thedescribed techniques are performed in a different order, and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner and/or replaced or supplemented by othercomponents or their equivalents. Therefore, the scope of the disclosureis defined not by the detailed description, but by the claims and theirequivalents, and all variations within the scope of the claims and theirequivalents are to be construed as being included in the disclosure.

What is claimed is:
 1. A communication apparatus with a multiple-inputand multiple-output (MIMO) channel, the communication apparatuscomprising: a minimum mean square error (MMSE) detector configured toestimate quadrature amplitude modulation (QAM) symbols based on signalsreceived through the MIMO channel; a QAM demodulator configured todemodulate the estimated QAM symbols, and estimate a first posteriorprobability of each of encoded bits of the estimated QAM symbols; afirst module configured to remove a first prior probability of each ofthe encoded bits from the first posterior probability to generate softestimates of the encoded bits; a channel decoder configured to decodethe encoded bits based on the soft estimates, and generate an improvedposterior probability of each of the encoded bits; a second moduleconfigured to generate a second prior probability of each of the encodedbits based on the improved posterior probability, the second priorprobability being a first prior probability of each of encoded bits ofQAM symbols in a next iteration cycle; and a hard-decision estimatorconfigured to generate a sequence of hard estimates of information bitsbased on the improved posterior probability.
 2. The communicationapparatus of claim 1, wherein: the first posterior probability comprisesa logarithm ratio of posterior probabilities of each of the encodedbits; and the first prior probability comprises a logarithm ratio ofprior probabilities of each of the encoded bits.
 3. The communicationapparatus of claim 1, wherein: the first prior probability comprises anatural logarithm of a value obtained by dividing a probability that ak-th encoded bit in an n-th QAM symbol is 1, by a probability that thek-th encoded bit in the n-th QAM symbol is −1; n is an integer that isgreater than or equal to 1, and less than or equal to N; k is an integerthat is greater than or equal to 1, and less than or equal to K; and Ndenotes a number of the QAM symbols, and K denotes a number of bits ineach of the QAM symbols.
 4. The communication apparatus of claim 1,wherein: the improved posterior probability comprises a logarithm ratioof improved posterior probabilities of each of the encoded bits; thesecond module is configured to remove the soft estimates from theimproved posterior probability to generate the second prior probability;and in response to the improved posterior probability being generated atleast one time, the first module is further configured to generate softestimates of the encoded bits in the next iteration cycle based on thesecond prior probability as the first prior probability in the nextiteration cycle.
 5. The communication apparatus of claim 1, furthercomprising: a remodulator configured to generate a first mathematicalexpectation and a first variance of the QAM symbols based on theimproved posterior probability; and a third module configured to comparethe first variance with a third variance generated after estimation ofQAM symbols in a previous iteration cycle, and generate input parametersof the MMSE detector based on a result of the comparing.
 6. Thecommunication apparatus of claim 5, wherein: the input parameterscomprise a second mathematical expectation and a second variance of theQAM symbols; a third mathematical expectation is generated after theestimation of the QAM symbols in the previous iteration cycle; inresponse to the first variance being less than the third variance, thethird module is configured to generate the second mathematicalexpectation based on the first mathematical expectation, the firstvariance, the third mathematical expectation, and the third variance,and generate the second variance based on the first variance and thethird variance; and in response to the first variance being greater thanor equal to the third variance, the third module is configured togenerate the second mathematical expectation and the second variance ina current iteration cycle, as a second mathematical expectation and asecond variance of QAM symbols in the previous iteration cycle.
 7. Thecommunication apparatus of claim 6, wherein: the MMSE detector isconfigured to apply an MMSE linear filter to a symbol vector of the QAMsymbols to generate an MMSE estimation mathematical expectation and anMMSE estimation variance of the symbol vector, generate a thirdmathematical expectation to be used in the next iteration cycle, basedon the second mathematical expectation, the second variance, the MMSEestimation mathematical expectation, and the MMSE estimation variance,generate a third variance to be used in the next iteration cycle, basedon the second variance and the MMSE estimation variance, and estimateQAM symbols in the next iteration cycle in response to the thirdmathematical expectation and the third variance being generated; and thethird mathematical expectation and the third variance to be used in thenext iteration cycle are input to the QAM demodulator in the nextiteration cycle.
 8. The communication apparatus of claim 7, wherein theQAM demodulator is further configured to: estimate a first posteriorprobability of each of the encoded bits in the next iteration cyclebased on the third mathematical expectation and the third variance to beused in the next iteration cycle.
 9. The communication apparatus ofclaim 6, wherein, in response to the third mathematical expectation andthe third variance being initially generated, the third module isconfigured to set the second mathematical expectation to 0, and set thesecond variance to a unit variance.
 10. The communication apparatus ofclaim 1, further comprising: a deinterleaver configured to deinterleavethe encoded bits before the encoded bits are input to the channeldecoder; and an interleaver configured to interleave soft bits outputfrom the channel decoder.
 11. A method in a communication apparatus witha multiple-input and multiple-output (MIMO) channel, the methodcomprising: estimating quadrature amplitude modulation (QAM) symbolsbased on signals received through the MIMO channel; demodulating theestimated QAM symbols; estimating a first posterior probability of eachof encoded bits of the estimated QAM symbols; removing a first priorprobability of each of the encoded bits from the first posteriorprobability to generate soft estimates of the encoded bits; decoding theencoded bits based on the soft estimates; generating an improvedposterior probability of each of the encoded bits; generating a secondprior probability of each of the encoded bits based on the improvedposterior probability, the second prior probability being a first priorprobability of each of encoded bits of QAM symbols in a next iterationcycle; and generating a sequence of hard estimates of information bitsbased on the improved posterior probability.
 12. The method of claim 11,wherein: the first posterior probability comprises a logarithm ratio ofposterior probabilities of each of the encoded bits; the first priorprobability comprises a logarithm ratio of prior probabilities of eachof the encoded bits; the improved posterior probability comprises alogarithm ratio of improved posterior probabilities of each of theencoded bits; the generating of the second prior probability comprisesremoving the soft estimates from the improved posterior probability togenerate the second prior probability; and the method further comprises,in response to the improved posterior probability being generated atleast one time, generating soft estimates of the encoded bits in thenext iteration cycle based on the second prior probability as the firstprior probability in the next iteration cycle.
 13. The method of claim11, further comprising: generating a first mathematical expectation anda first variance of the QAM symbols based on the improved posteriorprobability; and comparing the first variance with a third variancegenerated after estimation of QAM symbols in a previous iteration cycle;and generating input parameters for estimation of the QAM symbols in thenext iteration cycle based on a result of the comparing.
 14. The methodof claim 13, wherein: the input parameters comprise a secondmathematical expectation and a second variance of the QAM symbols; athird mathematical expectation is generated after the estimation of theQAM symbols in the previous iteration cycle; the generating of the inputparameters comprises, in response to the first variance being less thanthe third variance, generating the second mathematical expectation basedon the first mathematical expectation, the first variance, the thirdmathematical expectation, and the third variance, and generating thesecond variance based on the first variance and the third variance; andthe generating of the input parameters comprises, in response to thefirst variance being greater than or equal to the third variance,generating the second mathematical expectation and the second variancein a current iteration cycle, as a second mathematical expectation and asecond variance of QAM symbols in the previous iteration cycle.
 15. Themethod of claim 14, wherein the estimating of the QAM symbols comprises:applying a minimum mean square error (MMSE) linear filter to a symbolvector of the QAM symbols to generate an MMSE estimation mathematicalexpectation and an MMSE estimation variance of the symbol vector:generating a third mathematical expectation to be used in the nextiteration cycle, based on the second mathematical expectation, thesecond variance, the MMSE estimation mathematical expectation, and theMMSE estimation variance; generating a third variance to be used in thenext iteration cycle, based on the second variance and the MMSEestimation variance; and estimating QAM symbols in the next iterationcycle in response to the third mathematical expectation and the thirdvariance being generated.
 16. The method of claim 15, furthercomprising: estimating a first posterior probability of each of theencoded bits in the next iteration cycle based on the third mathematicalexpectation and the third variance to be used in the next iterationcycle.
 17. The method of claim 14, further comprising, in response tothe third mathematical expectation and the third variance beinginitially generated: setting the second mathematical expectation to 0,and setting the second variance to a unit variance.
 18. An apparatuscomprising: a processor configured to estimate quadrature amplitudemodulation (QAM) symbols based on signals, demodulate the estimated QAMsymbols, estimate a posterior probability of each of encoded bits of theestimated QAM symbols, remove a prior probability of each of the encodedbits from the posterior probability to generate soft estimates of theencoded bits, and decode the encoded bits based on the soft estimates.19. The apparatus of claim 18, wherein the processor is furtherconfigured to: generate an improved posterior probability of each of theencoded bits; generate another prior probability of each of encoded bitsof QAM symbols in a next iteration cycle based on the improved posteriorprobability; and generate a sequence of hard estimates of informationbits based on the improved posterior probability.
 20. The apparatus ofclaim 19, wherein the processor is further configured to: generate afirst mathematical expectation and a first variance of the QAM symbolsbased on the improved posterior probability; generate a secondmathematical expectation and a second variance of the QAM symbols basedon the first mathematical expectation, the first variance, a thirdmathematical expectation of QAM symbols in a previous iteration cycle,and a third variance of the QAM symbols in the previous iteration cycle;and estimate QAM symbols in the next iteration cycle based on the secondmathematical expectation and the second variance.